Localizing potentially active post-transcriptional

regulations in the Ewing’s sarcoma gene regulatory

network.

Tatiana Baumuratova, Didier Surdez, Bernard Delyon, Gautier Stoll, Olivier

Delattre, Ovidiu Radulescu, Anne Siegel

To cite this version:

Tatiana Baumuratova, Didier Surdez, Bernard Delyon, Gautier Stoll, Olivier Delattre, et al..Localizing potentially active post-transcriptional regulations in the Ewing’s sarcoma gene regu-latory network.. BMC Systems Biology, BioMed Central, 2010, 4 (1), pp.146. <10.1186/1752-0509-4-146>. <inserm-00984711>

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METHODOLOGY ARTICLE Open Access

Localizing potentially active post-transcriptionalregulations in the Ewing’s sarcoma generegulatory networkTatiana Baumuratova1,2,3*, Didier Surdez4,5, Bernard Delyon2,3, Gautier Stoll6,7,8, Olivier Delattre4,5,

Ovidiu Radulescu2,3,9,10,11, Anne Siegel12,13,11

Abstract

Background: A wide range of techniques is now available for analyzing regulatory networks. Nonetheless, most of

these techniques fail to interpret large-scale transcriptional data at the post-translational level.

Results: We address the question of using large-scale transcriptomic observation of a system perturbation to

analyze a regulatory network which contained several types of interactions - transcriptional and post-translational.

Our method consisted of post-processing the outputs of an open-source tool named BioQuali - an automatic

constraint-based analysis mimicking biologist’s local reasoning on a large scale. The post-processing relied on

differences in the behavior of the transcriptional and post-translational levels in the network. As a case study, we

analyzed a network representation of the genes and proteins controlled by an oncogene in the context of Ewing’s

sarcoma. The analysis allowed us to pinpoint active interactions specific to this cancer. We also identified the parts

of the network which were incomplete and should be submitted for further investigation.

Conclusions: The proposed approach is effective for the qualitative analysis of cancer networks. It allows the

integrative use of experimental data of various types in order to identify the specific information that should be

considered a priority in the initial - and possibly very large - experimental dataset. Iteratively, new dataset can be

introduced into the analysis to improve the network representation and make it more specific.

BackgroundNetwork modeling and data analysis in Cancer Systems

Biology

During the last decade, interest in using network models

for elucidating mechanisms of disease has constantly

increased [1]. In particular, identifying the pathways that

are responsible for malignancy is an important challenge

in Cancer Systems Biology [2,3]. Although it is now

accepted that cancer is a genetic disease, the levels of

gene expression remain, for many reasons, unreliable

indicators of causation [4]. First, the genetic perturba-

tions produce a multitude of changes, not all related to

the phenotype. Second, the mutated genes initiating the

processes are not detectable as differentially expressed.

Last but not least, important modifications of the path-

ways result from post-translational interactions that are

independent of the changes at the mRNA level.

Information on protein-protein and protein-DNA

interactions has recently become available for human

interaction pathways. Many groups combine literature

information and machine learning to build network

models for disease. It has been proposed that networks

can be used as filters to identify genes implicated in

cancer. For instance, Chuang and colleagues used net-

work models to improve markers for tumor classifica-

tions [3]. They identified mutated genes in cancer from

their effect on connected sub-networks of differentially

expressed genes. The sub-networks are proposed as

classifiers of tumors and could also serve to generate

new hypotheses about the disease. A similar idea has

been investigated by Ergun and colleagues [2]. They

identified groups of genes whose expressions are most

affected by disease. In order to find these genes, the

* Correspondence: tatiana.baumuratova@uni.lu1Systems Biology Group, Life Science Research Unit, University of

Luxembourg,162A Avenue de la Faiencerie, Luxembourg, L-1511,

Luxembourg

Full list of author information is available at the end of the article

Baumuratova et al. BMC Systems Biology 2010, 4:146

http://www.biomedcentral.com/1752-0509/4/146

© 2010 Baumuratova et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.

dynamics of the network is modeled by simplified differ-

ential equations. The disease is supposed to affect tran-

scription rates by multiplying them by a gene-dependent

factor. Estimating this factor from data allows one to

rank genes according to a z-score representing the influ-

ence of the disease. Another method for determining

the genes most affected by disease has been applied to

cancer by Mani and colleagues [5]. Mutual information

(MI) quantifies the degree of dependence between inter-

acting genes. By computing the change of MI induced

by various tumor phenotypes in cohorts of patients, one

can assign to each tumor phenotype a set of genes that

are most affected. Various network-based classifiers of

tumors have been proposed elsewhere [6,7].

Constraint-based approaches for hypothesis generation

All approaches mentioned above face difficulties in the

quality of the network representation. Indeed, in any

network-based study of disease, the first step is the net-

work construction. “Gold standard” evidence from

curated databases and from the literature allow integrat-

ing a large amount of experimental and computational

evidence. Such evidence is gathered in a model, often

represented by an interaction graph that is prone to

incompleteness and uncertainty. Mutual information [5]

or other machine-learning methods [2,3] can be used to

fill in gaps in the network or, alternatively, to discard

interactions if their presence is not supported by data.

Nonetheless, the logical implications of the confronta-

tion between network models and data are insufficiently

explored by these methods.

A different class of approaches, less developed in can-

cer studies, uses model-checking and constraint-based

analysis to test and exploit the logical consistency

between model and data. Several types of queries can be

performed. They can be dynamic, although using differ-

ent temporal logics, like those implemented in the Bioc-

ham [8], BioNetGen [9] or GeneNetAnalyser [10]

softwares. Queries may also be static, particularly in the

case of middle and large-scale networks. For instance,

Bowers and colleagues rely on static logic relationships

to investigate protein network organization [11]. Baum-

bach and colleagues also used static rules to perform

predictions on corynebacterial regulatory networks [12].

We have recently designed a tool for constraint-based

analysis of interaction networks [13] named BioQuali

which aims at automatizing such approaches. The tool

solves large systems of qualitative equations which con-

nect the variations of a node to the variations of its pre-

cursors in the interaction graph. Applied to network

models, BioQuali can validate the existence of interac-

tions in a network and predict the variations of nodes

that are not directly measured. In case of conflict

between model and data, the origin of the conflict is

localized by analyzing the propagation of the constraints.

Then, new experiments can be suggested to check the

new hypotheses [14]. In this context, constraint-based

analysis complements statistical approaches in hypoth-

esis generation.

Alternative network representation coping with realistic

interactions and data

The main difficulty faced when applying such formal

methods lies in the network representation. Indeed,

most of models studied in this context are built from

curated databases or the literature. According to the

level of detail in the knowledge and the size of networks

at hand, most models do not discriminate between

mRNA and protein levels. The variables (node attri-

butes) are mRNA levels observed with microarrays.

However, protein levels are not always correlated to

mRNA levels, especially in cancer systems. In such

situations, it is vital to distinguish between transcrip-

tional, post-transcriptional and post-translational inter-

actions. Furthermore, new types of nodes should be

used whose attributes, though constrained by the net-

work, are not available in the experiment. Predicting the

attributes of these nodes (for instance, protein activities)

is essential for assessing the effectiveness of the

interactions.

In this paper we propose a constraint-based analysis of

a cancer network model. Our main concern is to iden-

tify and account first for uncorrelated protein and

mRNA levels, second for post-translational interactions.

We automatically modify the network representation so

that each gene satisfying our given criteria is represented

by two nodes, an mRNA and a protein node. Using the

BioQuali tool, we check consistency between the data

and two descriptions of the system: the initial network

built from a curated database and the modified network

with distinct interaction levels. We investigate the

results of the constraint-based analysis tool to predict

protein activities and identify active regulations in the

network. Within this framework, the notion of activity is

understood as interactions in the network that are logi-

cally required to obtain a given set of observations.

Lastly, we confront the results of our analysis to new

data in order to refine the model.

Other network representation studies, such as those

performed by Mani and colleagues [5], have suggested

distinctions between types of proteins - transcription

factors, non-TF and modulators according to their func-

tion in the cell. However, the representation of network

interactions and their relation to data is not equivalent

to ours. The network model in our constraint-based

approach is a signed interaction graph. The information

on the sign of interactions (inhibition, activation) is

essential for our analysis. MI-based networks use

Baumuratova et al. BMC Systems Biology 2010, 4:146

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oriented interactions for which the sign information is

not important. More importantly, in these models post-

translational modifications are represented as modula-

tions of the interactions between transcription factors

and their targets. Our decision to separate protein and

mRNA nodes allows a more rigorous analysis of the

constraints. It can exploit data of both mRNA and pro-

tein types, and therefore performs hypothesis generation.

Analysis of a cancer network

To test our method, we used it to study Ewing’s sar-

coma. Ewing’s sarcoma is the second most common

malignant bone tumor in children and young adults.

Delattre and colleagues showed that it is associated in

more than 80% of cases with the t(11;22)(q24;q12) chro-

mosomal translocation [15,16]. The latter induces the

expression of the chimeric protein composed of the N

terminal part of the EWS gene with the ETS family

member FLI1 c-terminal part [17]. Consequently, EWS-

FLI acts as an aberrant transcriptional activator/repres-

sor in Ewing’s sarcoma by altering the expression of

specific target genes [18,19]. Our decision to analyze

Ewing’s sarcoma is motivated by two aspects. First, the

key genetic perturbation of the Ewing network is known

to be EWS-FLI1, a transcription factor present in the

majority of Ewing’s tumors [20]. Second, large datasets

accumulated on this cancer are available, making it of

particular interest for our approach. In cell lines, the

changes of genetic program induced by siRNA inhibi-

tion of the oncogene lead to cell cycle arrest in G0/G1.

Upon reactivation, most of the expression levels change

and the phenotypes are reversed. Altogether, this pro-

vides quite reliable datasets for the study of the network

perturbation. However, the precise regulatory pathways

of the EWS-FLI1 oncogene are not yet fully elucidated.

Identifying gene interactions that are potentially

involved in the regulation of EWS-FLI1-related path-

ways are of great interest for studying Ewing’s sarcoma.

MethodsGene expression data, Ews-Fli1 regulatory network

Data on gene expression upon Ews-Fli1 knock down/rescue

in Ewing’s cancer cells

Transcriptome time-series data were obtained using

Affymetrix U133A microarray as published in [21]. In

this paper, Ewing cells were profiled upon EWS-FLI1

knocked down (with retroviral-mediated RNAi EF-2-

RNAi construct) and then rescued based on a tetracy-

cline-inducible EWS-FLI1 cDNA. Predicted variations of

gene expression during inhibition of EWS-FLI1 were

validated with additional experiments as follows. For

RT-QPCR and Western blotting experiments, EWS-FLI1

transcript was silenced and reactivated using a tetracy-

cline inducible shEWS-FLI1 specific construct in clones

derived from the A673 cell lines. Levels of IGF2 mRNA

(taqman Hs01005963_m1, Applied biosystems) and

FASLG mRNA (forward primer: ggaaagtggcccatttaaca

reverse primer: ccagaaagcaggacaattcc) were measured by

real-time quantitative polymerase-chain reaction (RT-

QPCR, normalized to RPLP0). Western blotting was

performed with IGF2 (AB9574, Abcam) and FASLG

(AB15285, Abcam) antibodies.

Regulatory network of EWS-FLI1 chimeric oncogene

An annotated gene regulatory and signaling model was

designed by our colleagues from Institut Curie [22]. The

model involves 130 genes selected according to the

strength of their response on the inhibition/reactivation

of EWS-FLI1. Using information from BIOBASE [23]

and manual curation of the literature, around 300 inter-

actions were selected to describe signaling pathways that

regulate key functions involved in tumor progression

(cell cycle phase transitions, apoptosis and cell migra-

tion). Products in the networks correspond to genes,

proteins, phosphorylated proteins - such as RB1_pho-

sphorylated - and protein complexes such as the com-

plex made of CCNB1 and CDK2. Due to lack of precise

knowledge, additional nodes were added to describe the

effects of families of proteins - such as the RAC family

which includes RAC1, RAC2 and RAC3. Interactions

issued or targeting such a node gather all interactions

known for at least one element in the family. Finally,

the network included some nodes to describe the phe-

notypic effects of the oncogene - apoptosis, cell migra-

tion, cell cycle anaphase, -G2, -M and -S phases.

From this annotation of the network we have

extracted an interaction graph as follows. Nodes of the

graph are given by the products that appear in the initial

model. Every interaction in the initial annotated model

is mapped to a labeled edge of the interaction graph.

Every edge represents a positive (‘+’ meaning up-regula-

tion), negative (‘-’, down-regulation) or dual (‘?’, the

actual regulation is unknown) influence of a source

node (precursor) on a target node (successor) (see Addi-

tional file 1: Interaction graph adapted from the original

network). Each interaction is also annotated with respect

to the type of interaction (transcriptional or post-trans-

lational, not shown). The interaction graph was further

modified according to the method proposed below to

enhance transcriptional and post-translational effects

(see the forthcoming section “First add-on: changing the

descriptive level of the network” and Additional File 2:

Modified interaction graph).

Extracting average trend of gene response to the oncogene

inhibition

We used a dedicated gene-level summarization and data

filtering process to analyze the Affymetrix data pub-

lished in [21]. Our goal was to capture the average

trend of the response of the network nodes to oncogene

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inhibition, that is, to identify nodes which are either cor-

related or anti-correlated to the oncogene inhibition

expression pattern. A pre-normalization procedure was

performed in [21]: (A) Checking for mis-targeted and

non-specific hybridization; (B) Background subtraction,

in order to remove the fluorescence induced by factors

other than hybridization of RNA to the chip. (C) Nor-

malizing the data using a Robust Multiarray Average

(RMA) method to remove systematic bias such as plat-

form-specific variations or the influence of non-biologi-

cal factors.

Starting from these data, a gene-level summarization

was performed by grouping the probe sets correspond-

ing to the same gene. Within such groups, all probe set

lines were kept instead of substituting them with the

single mean or median value. This allowed capturing

the statistical significance of the gene response including

all its transcript variances. Such gene groups of the full

microarray were then filtered to select only those genes

relevant for the network. An outlier removal procedure

was applied to the selected gene-level grouped data.

Data points were considered as outliers if their residuals

were larger than two standard deviations (SD) of the

gene dataset. The residuals were computed as the dis-

tance from the regression line built for each gene to

each of the gene set data points.

Finally, genes were arranged according to the signifi-

cance of their responses to the oncogene inhibition in

the Affymetrix microarrays [21]. To that end, the

response curves of the transcripts corresponding to a

gene were approximated with a linear function using lin-

ear regression. A gene was considered as significantly

responding to the oncogene inhibition if the regression

line fitted its response curves with a statistical signifi-

cance level of less than 5% and if the slope of the regres-

sion line significantly differed from zero. The latter

condition was checked indirectly using a two-sample

location test (Student’s t-test), which was performed on

the initial and final points of the time series. The signifi-

cance threshold was again fixed at 5%. Gene variation

signs were provided either by signs of slopes (for linear

regression) or by signs of variations (for Student’s t-test),

given that both parameters are statistically significant (p-

value less than 5%). A final adjustment to multiple pair-

wise tests - Holm-Bonferroni method, family-wise error

rate, false discovery rate - was not considered as relevant

since the list of genes had been previously reduced to a

much smaller one, corresponding to the studied network.

Constraint-based analysis: automatic reasoning tool and

its add-ons

BioQuali tool

We used an open-source software tool named BioQuali

to perform the qualitative analysis of EWS-FLI1

regulatory network [13]. BioQuali assesses the compat-

ibility between the network topology and the expression

variations induced by a disturbance of a system. It per-

forms automatic reasoning by propagating observations

along the network - as usually done when reasoning

about regulations - and checking whether this propaga-

tion either yields a contradiction or generates new

deductions. The tool is publicly available at http://geno-

web1.irisa.fr/Serveur-GPO/outils/interactionNetwork/

BIOQUALI/ [13].

The propagation of information is modeled by estab-

lishing a set of rules connecting the sign of a node var-

iation to the signs of its precursors’ variations in the

network. The main rule used in this paper was as fol-

lows: “the sign of a variation of a node cannot be oppo-

site to the sign of variations of all the influences it

gathers from its precursors in the network”. This rule

was mathematically proven to be valid if the initial and

the final states of the network are steady states of the

systems [24]. Note however that stronger rules may be

used when precise knowledge on interaction is available

[14]. The full set of rules is encoded as a system of qua-

litative constraints over its inputs (interaction graph and

observations on node variations). It may therefore be

considered as a constraint-based modeling tool. Solving

such a system is computationally difficult. The BioQuali

tool uses a dedicated constraints solver based on a deci-

sion diagram to overcome the computational difficulty

and solve systems of biological constraints in a reason-

able time. More details may be found in the software

publication [13,25].

The complete system of rules is analyzed to decide

whether the interaction graph is compatible with the

input data. Consistency means first that all the interac-

tions in the graph are in logical agreement with each

other and, second, that they do not conflict with experi-

mental observations. In case of disagreement, BioQuali

points out the inconsistent parts of the interaction

graph. In case of consistency, BioQuali generates a set

of predictions. They correspond to variations of non-

observed nodes that can be deduced from the observed

variations - that is, available data - by applying the gen-

eric biological rule introduced earlier. In Figure 1, we

detail a deduction process leading to such predictions.

Notice however that the BioQuali tool does not expli-

citly compute all the steps in this process: the software

encodes the steps into equations and then solves the

resulting system with efficient methods [25].

Two other examples of predictions are shown in Fig-

ure 2. They may be classified into two types with respect

to the constraint propagation as follows:

• Forward predictions are those which are straight-

forwardly deduced from precursors of a predicted

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node. Such predictions reflect the consequence of

the concerted action of the precursors on a pre-

dicted node in the network. If the actions of all the

precursors are known to have the same sign, the

node will potentially vary in the direction that is

imposed by this concerted action of its precursors.

• Backward predictions are deduced from the succes-

sors of a predicted node. These predictions represent

the only possible explanations of the associated

experimental observations and (or) the topology of

the network model. In contrast to forward ones,

backward predictions do not reflect the smooth pro-

pagation of an interaction along the network. They

may rather be seen as the result of an automatic glo-

bal reasoning to fill in gaps between observations on

node variations, in order to find the only possible

reasons for such observations.

Sorting predictions according to their significance can

be performed in different ways. A statistical significance

may be computed by pointing out those predictions that

are very specific to the dataset - slightly changed dataset

will no more generate a prediction. This approach was

used in [14] to test the robustness of a network and the

validity of reasoning rules. An alternative is to consider

all the predictions if they are not too numerous. As we

will detail in the sequel, we will consider all predictions

and favor those providing information on post-transla-

tional processes.

First add-on: changing the descriptive level of the network

The initial network represents a summary of our biolo-

gical knowledge about the pathways putatively influ-

enced by the oncogene. The nodes of this network

represent proteins, family of proteins, protein com-

plexes, and phenotypes. We have modified the network

in order to obtain an interaction graph, i.e. an oriented

signed graph whose arrows (directed edges) represent

interactions. An arrow connects two nodes if the tail

node influences the production of the head node. We

used the following criteria to modify the node represen-

tation of the initial network according to the transcrip-

tional or post-translational nature of interactions. This

Figure 1 BioQuali reasoning: logical steps leading to predictions. The tool encodes knowledge and observations into a system of

qualitative equations which is solved with an efficient algorithm [25]. The algorithm generates the complete set of solutions of these equations

and identifies invariants of the set of solutions, that is, node values that are constant throughout the entire set of solutions. These invariants are

called predictions. They can also result from the logical deduction process detailed above. The tool computes predictions without detailing the

reasoning steps, which would be impossible for large scale systems. Step 1 (backward deduction). The node mRNA AKT1 is observed as up-

regulated. It is regulated by IGF1 only. Therefore IGF1 should be up-regulated to explain the observation. Step 2 (backward deduction). IGF1

increase cannot be derived from an increase of its transcriptional activity since its mRNA is down-regulated. The only possible explanation is a

decrease of its inhibitor IGFBP3. Step 3 (backward deduction). Consequently, the only regulator of IGFBP3 should be up-regulated. Step 4

(forward deduction). All incoming regulations on PIK3C tend to increase it. This should result in an increase of the node activity. Final step. No

additional input or former deduction propagates. Deductions are all in agreement with each other. They are denoted as predictions and the

process ends. Alternatively, if a decrease of PIK3C is observed or deduced, the full process would fail, all deductions would be discarded and an

inconsistency diagnosis would be generated.

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allowed us to point out predictions which are relevant

from the post-translational viewpoint.

• Whenever possible, families of proteins are split

into representatives.

• For each protein which is a target of at least one

transcriptional regulation or which is coded by a

gene observed at the transcriptional level, the modi-

fied network will contain two nodes: the mRNA and

the protein. Alternative splicing can be taken into

account at this level, because several proteins can be

coded by the same gene and thus emerge from the

same mRNA.

• The mRNA nodes are targets of all transcriptional

regulations and up-regulate protein nodes - see Fig-

ure 2 for details. The underlying assumption here is

that increasing (or decreasing) the production of an

mRNA tends to increase (or decrease) the produc-

tion of the corresponding protein.

• The other protein nodes (not targets of post-trans-

lational interactions or whose mRNA are not

observed) remain as such (do not generate an

mRNA node).

• In the interaction graph, the precursors of protein

complexes are the constituent proteins.

• All protein-protein interactions are designated as

post-translational.

Second add-on: post-processing of BioQuali results and

classification of predictions

The post-processing of BioQuali predictions was

designed to enhance information on the activity of post-

translational interactions. We pointed out those predic-

tions which contradict the naive dogma of a correlation

between mRNA and protein levels.

• Type I. Predicted protein variations opposing

mRNA variations. Assuming that the variation of a

protein is correlated to the variation of its mRNA

unless it is perturbed by a post-translation process, a

prediction of Type I suggests that a post-transcrip-

tional regulation in the network strongly reverses

the transcriptional production of the predicted

Figure 2 Types of inconsistencies caused by undifferentiated mRNA and proteins in the network representation. On both images A and

C, inconsistencies occur on the node ‘B’. The first example (A) is inconsistent because the product of gene expression, represented by node ‘B’ is

a target of both transcriptional (TR) and post-translational (P-TR) regulations, which is not possible since transcriptional interactions should target

mRNAs, while post-translational interactions target proteins. This inconsistency is removed by the differentiation between mRNAs (node

‘mRNA_B’) and proteins (node ‘B’) as shown on image B. The second type of inconsistency (C) is caused by the association of experimental data

on variation in mRNA with a node which should represent a protein because it is a target of post-translational regulations. This inconsistency

can also be avoided if mRNAs and proteins are distinguished in the network (D).

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protein. Type I predictions are usually obtained as

backward deductions.

• Type II. Predicted protein variations with non-sig-

nificant mRNA variations. Predictions of this type

are similar to predictions of Type I and suggest the

existence of active post-translation interaction. How-

ever, the argument in favor of active interaction is

weaker here than for Type I predictions. Indeed, a

non-significant mRNA variation can be due to an

inaccuracy in the dataset - such as a lack of sensitiv-

ity of the microarray technique used to detect the

variation of the predicted gene.

• Type III. Predicted protein variations correlated to

mRNA variations. This type of predictions is fre-

quently derived from forward deductions, especially

when the observed mRNA is the only precursor of the

predicted protein. Such predictions simply suggest that

the transcriptional interactions are dominating.

This classification is obtained by performing a com-

plete comparison of predictions about mRNA and pro-

tein nodes. This functionality will be included in a

forthcoming distribution of BioQuali software.

Results and DiscussionIdentification of significantly responsive genes

In the BioQuali setting, we call “observation” a gene

with significant variation. The set of observations used

for the qualitative analysis of the tumor oncogene net-

work was inferred from publicly available transcriptome

time series data from Smith and colleagues [21], see

Methods section. Time series provide the transient, that

is, intermediate values of the variables between the

initial and final states. Nonetheless, the BioQuali formal-

ism is not concerned with the details of the transients,

but only with the average trend of the response: increase

or decrease between the two states.

Such an average trend has a meaning also for variables

whose level in individual cells oscillates, as for instance

genes and proteins controlling the cell cycle. For these

variables, the average trend characterizes the change in

the population of cells (in our case the inhibition of the

oncogene induces a stop of proliferation in G1 and up-

regulates G1 specific genes). In both cases, a linear

regression method was applied (see Methods section) as

the simplest approximation which captures the average

trend of the response.

The analysis of the microarray time series data is sum-

marized in Table 1 and in Additional file 3: Results of

statistical analysis of microarray data [21]). It contains

33 genes of the network significantly responsive on

EWS-FLI1 inhibition.

This set of significant variations was complemented

with variations over non-transcriptional products,

including the variation of EWS-FLI1 protein and varia-

tions of ‘phenotypic’ nodes (representing apoptosis, cell

migration, cell cycle anaphase, -G2, -M and -S phases).

These variations were deduced from biological observa-

tions (see Additional file 4: Set of experimental

observations).

Consistency analysis of EWS-FLI1 regulatory network

We performed a consistency analysis of the EWS-FLI1 regu-

latory network two times: first in its original shape deduced

from BIOBASE and second time in its modified shape to

enhance post-translational processes. As an input dataset to

BioQuali we considered the observations resulting from the

inactivation of the oncogene performed by Smith and collea-

gues [21]. Since EWS-FLI1 is down-regulated during this

experiment, the variation of EWS-FLI1 was set to ‘-’.

According to the Identification of significantly responding

genes performed above, genes and proteins which are anti-

correlated to EWS-FLI1 and are up-regulated upon the

oncogene silencing have ‘+’ as a sign of variation, while cor-

related genes and proteins have ‘-’ as a sign of variation.

We first used the BioQuali tool to analyze the interac-

tion network directly inferred from EWS-FLI1 BIOBASE

regulatory network, prior to changing its descriptive

level: at this stage, mRNA and protein nodes were not

separated. The network was compared to the set of

observations resulting from transcriptome time series

data analysis. The analysis showed that this network

with non-distinguished gene products has two inconsis-

tent parts on nodes ‘(PIK3R.)’ and ‘Cell Migration’ (Fig-

ure 3). Both cases of inconsistency resulted from

experimental observations on their preceding nodes. For

both (PIK3R.) and Cell Migration nodes, the observed

variation cannot be deduced from variations in their

precursors (PDGFRB for (PIK3R.), see Figure 3A, and

(RAC.) and (RHO.) for Cell Migration, Figure 3B). This

suggests that the observed inconsistencies are due to

deficiency in the network topology representation: the

experimental observations were made at mRNA level

(microarray data), but they are associated with nodes

that represent proteins (interactions of the inconsistent

parts are post-translational). This supports our proposi-

tion of differentiation between transcriptional and post-

translational products in regulatory networks.

The original network was then modified by separating

mRNAs and proteins as described in the Method section

(Additional file 2: Modified interaction graph). BioQuali

analysis of the new enriched interaction network with

the observation dataset showed that the new network is

consistent with the experimental observations.

Predictions on the enriched network

The analysis of the enriched network using BioQuali

resulted in 31 predictions about variations

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corresponding to different node types, as follows: 4 pre-

dictions made about mRNAs, 17 predictions made

about proteins (with one protein representing a phos-

phorylated protein), 5 predictions made about nodes

representing protein complexes and 5 predictions made

about nodes representing protein groups/families (Table

2). All variations predicted by BioQuali - except those

about protein complex and phenotypic nodes - were

compared to experimental observations on the expres-

sion levels of their corresponding mRNAs, according to

the classification of predictions introduced in the

Method section. This classification identified eight pre-

dictions which allow us to discuss the existence of active

regulations. Five predictions are of Type II - CDC2,

IGF2, RB1-phosphorylated, TGFBR2 and TP73- and

three predictions are of Type I for proteins or families

-(RAC.), IGF1 and IGFBP3. The remaining 14 predic-

tions on proteins and protein groups/families are of

Type III.

From the 14 predictions of Type III, a path of predic-

tions was selected for experimental validation of the

approach. We considered FASLG as an important node

to be checked since the BioQuali logics applied to its

transcript variations both implies that the protein

FASLG is increasing and that JUN, the only precursor

to FASLG mRNA, is increasing as well - see Figure 4B

for details. Results of quantitative real-time PCR con-

firmed that FASLG mRNA anti-correlates with EWS-

FLI1 (Figure 5), inducing an increasing variation of the

transcript during the oncogene inhibition. Western blot

on FASLG protein (Figure 6) confirms the ‘forward’ pre-

diction ‘FASLG = ‘+”.

Type II predictions

Type II predictions are more informative than those of

Type III since they correspond to proteins which

mRNAs were not observed with a significant p-value

during the oncogene inhibition. This suggests that a

phenomenon occurs at the post-translational level which

is not initiated at the transcriptional level.

The prediction about the TGFBR2 protein is a direct

consequence of another prediction, about its mRNA.

Indeed, TGFBR2 mRNA is predicted as ‘+’ by a for-

ward prediction sourced in its inhibition by the inhib-

ited oncogene EWS-FLI1, its only precursor (Figure 4A

and Table 2). The production of TGFBR2 is regulated

by its transcript only, and is therefore activated as well.

Table 1 Significantly varying products upon EWS-FLI1 oncogene silencing

Node name Variation Node name Variation Node name Variation

mRNA_RAS + mRNA_ECM1 + mRNA_RBL2 -

mRNA_CCNE_ + mRNA_ECM2 + mRNA_SKP2 +

mRNA_NFKB_ + mRNA_FAS - mRNA_SOS2 +

mRNA_PIK3C_ + mRNA_FASLG + mRNA_TNFAIP3 +

mRNA_PIK3R_ - mRNA_IER3 + mRNA_TNFRSF1A +

mRNA_RAC_ + mRNA_IGF1 - Phenotypic_observations

mRNA_RHO_ + mRNA_IGFBP3 +

mRNA_TGFB_ + mRNA_JUN + EWS-FLI1 -

mRNA_TNF_ + mRNA_MAPK8 + Cell_Cycle_Anaphase -

mRNA_AKT1 + mRNA_MYC - Cell_Cycle_G2 -

mRNA_CDKN1A + mRNA_MYCBP - Cell_Cycle_M -

mRNA_CDKN1C + mRNA_PDGFRB + Cell_Cycle_S -

mRNA_CYCS - mRNA_PRKCB1 - Cell_Migration -

mRNA_E2F5 - mRNA_RASA1 + Apoptosis +

List of observations used as an input set for BioQuali. Table contains the list of significantly varying genes resulting from the analysis of microarray data [21]. The

last seven lines of the third column contain additional variations on so-called ‘phenotypical nodes’ and on the EWS-FLI1 protein. They summarize the current

literature-derived knowledge on cell behavior upon silencing EWS-FLI1 oncogene.

Figure 3 Inconsistencies in the original interaction graph. The

inconsistencies indicate parts of the network where the original

regulatory model fails to satisfy the generic biological rule of

BioQuali. In part A, node (PIK3R.) has only one precursor, PDGFRB,

and its variation (PDGFRB = ‘+’) cannot explain the variation of

(PIK3R.) ((PIK3R.) = ‘-’). In part B, the variation of the phenotypic

node ‘Cell Migration’ (’Cell Migration’ = ‘-’) cannot be explained by

either of the variations of its precursors: (RAC.) = ‘+’ and (RHO.) =

‘+’.

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This is in agreement with previously reported down-

regulation of TGFBR2 by EWS-FLI1 [26,21,27]. This

also points out a lack in sensitivity of the Affymetrix

data, which did not report a significant variation of

TGFBR2 mRNA.

Among the other Type II predictions, we would point

out the prediction about the protein TP73 as down-

regulated during the inhibition of EWS-FLI1. The

source of the prediction is the following: TP73 is the

only inhibitor of PDGFRB mRNA which is observed as

Table 2 Summary of EWS-FLI1 network analysis

Prediction Product Deductiontype

Functionaltype

Source of the prediction

Protein nodes

(PIK3C.) = + Forward Type III obs. mRNA (PIK3C.), pred. (.RAS), pred.

IGF1

(.RAS) = + Backward Type III pred. IGFBP3, obs. mRNA IGFBP3

(RAC.) = - Group of proteins Backward Type I obs. mRNA (RHO.) and obs. cell migration

(TGFB.) = + Forward Type III obs. mRNA TGFB

(TNF.) = + Forward Type III obs. mRNA TGFB

RB1_p = - Phosphorylatedprotein

Forward Type II pred. PRKCB1

CDC2 = - Forward Type II pred. (((CCNA.)_p):CDC2), pred. (CCNB1p:CDC2)

ECM1 = + Forward Type III obs. mRNA ECM1

ECM2 = + Forward Type III obs. mRNA ECM2

FASLG = + Forward Type III obs. mRNA FASLG

IER3 = + Forward Type III obs. mRNA IER3

IGF1 = + Backward Type I obs. mRNA ACT1

IGF2 = - Backward Type II obs. mRNA CDKN1C, pred. TP73

IGFBP3 = - Proteins Backward Type I pred. IGF1, obs. mRNA IGF1

JUN = + Backward Type III obs. mRNA FASLG

MYCBP = - Forward Type III obs. mRNA MYCBP

PRKCB1 = - Forward Type III obs. mRNA PRKCB1

RASA1 = + Forward Type III obs. mRNA RASA1

SKP2 = + Forward Type III obs. mRNA SKP2

TGFBR2 = + Forward Type II pred. mRNA TGFBR2

TNFAIP3 = + Forward Type III obs. mRNA TNFAIP3

TP73 = - Backward Type II obs. mRNA PDGFRB

mRNA and protein complex nodes

mRNA_BCL2 = + Forward - pred. IGFBP3

mRNA_IGF2 = - mRNAs Backward - pred. IGF2, pred. IGFBP3

mRNA_TGFBR2 = + Forward - obs. EWS-FLI1

mRNA_TP53 = - Forward - pred. JUN

((CCNA.):CDK2) =

-

Backward - obs. cell-cycle G2

((CCNA.)_p:CDC2)

= -

Forward - obs. cell-cyle M

((CCNA.)_p:CDK2)

= -

Protein complexes Forward - obs. cell-cycle M

(CCNB1:CDC2) = - Backward - obs. cell-cycle M

(CCNB1_p:CDC2) =

-

Forward - obs. cell-clycle anaphase

List of predictions deduced from EWS-FLI1 regulatory network. ‘Deduction’ corresponds to the direction of propagation of the regulation in the network. The first

part of the table corresponds to functional predictions about proteins. The ‘Functional type’ column shows the result of the post-processing classification of these

predictions detailed in the Method section. The second part of the table gathers relevant predictions about other nodes in the network, that is mRNA and

protein-complexes. Although they do allow discussing the dogma of a correlation between mRNA and protein levels, such predictions are used in the Results

section to discuss the relevance of predictions about proteins. For each prediction we indicate the source of prediction, which is the minimal set of nodes

(observed or predicted) that is needed for the deduction of the prediction. For instance, the first line reads as follows: the prediction about (PIK3C.) protein node

is a forward deduction from three sources: the observation on (PIK3C.) mRNA and the predictions about (.RAS) and IGF1 nodes.

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Figure 4 Representation of predictions in the network. This figure shows parts of the network where the predictions considered in the

section ‘Discussion’ are located. It helps backtracking the source of each prediction. Part A. Predictions on TGFBR2 mRNA and protein are

deduced from the observation on EWS-FLI1. The prediction about BCL2 mRNA is deduced from the prediction about IGFBP3, deduced from the

prediction about the IGF1 protein which, in turn, is deduced from the observation on AKT1 mRNA. The prediction about the TP73 protein is

deduced from the observation on PDGFRB mRNA and serves, together with the observation on CDKN1C mRNA, as a source of the prediction

about the IGF2 protein and subsequently - about IGF2 mRNA. The prediction about the family of proteins (.RAS) is deduced from the prediction

about the IGFBP3 protein and, together with the prediction about the IGF1 protein, is a source of the prediction on family of proteins (PIK3C.).

Part B. Prediction about the JUN protein is deduced from the observation on JUN mRNA and serves as a source for the prediction about the

TP53 mRNA. Prediction about the FASLG is a forward deduction from the observation on the FASLG mRNA. Part C. Prediction about the (RAC.)

family of proteins is deduced as the only possible explanation of the combination of observations on (RHO.) mRNA family and the phenotypic

node [Cell Migration]. Part D. Prediction on phosphorylation of RB1 is a forward deduction from the prediction on PRKCB1 which is deduced

from the observation on PRKCB1 mRNA. Part E. The prediction about the CDC2 protein is a result of the correlated interactions of its precursors,

predictions on complexes ((CCNA.)^ p:CDC2)) and (CCNB1^ p:CDC2).

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up-regulated (Figure 4A). This backward deduction sug-

gests that one of the incoming regulations of TP73 pro-

tein (activations by TP73 mRNA, MAPK1 and EP300

and inhibitions by MDM2 and MYC, not shown) may

be activated by the inhibition of the fusion oncogene.

This prediction about TP73 is of great importance since

it will result in a new prediction about IGF2 and its

mRNA and will finally allow us to propose network

refinements.

Type I predictions

Our post-processing of BioQuali output pointed out

three Type I predictions, which directly indicate active

post-translational regulations in the network. These are

predictions about IGF1, IGFBP3 and the (RAC.) family

of proteins which includes RAC1, RAC2 and RAC3.

(RAC.) case study

The prediction about (RAC) node results from a path of

reasoning in several steps. We refer to Figure 4C for

details. The prediction is a backward deduction from

the observation on the phenotypic node [Cell Migration]

([cell migration] = ‘-’). This phenotypic node has two

precursors: (RAC.) and (RHO.). Notice that (RHO.) also

has two precursors: its mRNA (observed as “+”) and the

node (RAC.). According to the logic of BioQuali, the

observed variation of [Cell Migration] must be explained

by one of the incoming regulations, which means that

one of the two precursors must have a variation ‘-’.

(RAC.) being set as ‘+’ would imply that (RHO.) receives

two positive influences, which would finally imply that

[Cell Migration] is positive, contradicting the

Figure 5 RT-QPCR data on FASLG, IGF2 and EWS-FLI1 time series. Quantification of FASLG, IGF2 and EWS-FLI1 was performed by RT-QPCR

in a time series experiment. EWS-FLI1 was silenced upon shRNA induction by addition of doxycycline in the media for up to 24 days. For the

recovery series, doxycycline was omitted from the media after the 10th day of culture (dashed lines).

Figure 6 Western blotting on IGF2 and FASLG. Western blotting

of FASLG, IGF2 and beta-actin in a time series experiment. EWS-FLI1

was silenced upon shRNA induction by addition of doxycyclin in

the media for up to 17 days.

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observation on this node. This makes the original

hypothesis ((RAC.) = ‘+’) impossible and therefore

(RAC.) is predicted as ‘-’.

However, the input dataset showed that the transcript

variation of the (RAC.) family is clearly positive. This

suggests that one of the post-translational regulations

on (RAC.) protein (in our network they come from

SOS2 protein and (PIK3R.) family) is active and reverses

the effect of the production of RAC transcripts.

IGF1 and IGFBP3 case study

The predictions about IGF1 and IGFBP3 will be dis-

cussed together since they are deeply connected.

The prediction about the IGF1 node is a backward

deduction from the observation on AKT1 mRNA (Fig-

ure 4A). The increase of IGF1 protein is the only possi-

ble explanation of its target - AKT1 mRNA - up-

regulation. The IGF1 node is the target of two incoming

regulations: from its transcript and from IGFBP3 pro-

tein. However, the transcript is observed as significantly

decreasing in the Affymetrix chip. This suggests that the

predicted behavior of IGF1 protein can only be

explained by the inhibition issued in the IGFBP3 pro-

tein. The effect of IGFBP3 competes with the protein

production from its transcripts and eventually reverses

it.

The predicted variation on IGFBP3 protein is also of

the backward type. It is a direct consequence of the var-

iation of IGF1 protein: since IGFBP3 is the only inhibi-

tor of IGF1, and since IGF1 must increase from the

previous analysis, we deduce that IGFBP3 protein is

decreasing during the experiment. Nonetheless, its tran-

scripts increase significantly during the experiment. This

suggests that the production of IGFPB3 results from a

competition between the translation of its transcript and

the regulation from the (RAS.) pathway. Here, the “com-

petition” is won by (RAS.).

This prediction was compared with the previously

reported data shown in Figure 6A of [28]. A discrepancy

appeared since these new data suggest that IGFBP3 pro-

tein is induced upon silencing of EWS-FLI1.

Towards a network refinement

Using predictions to propose a relevant set of additional

observations

In order to explain the discrepancy between the predic-

tion about IGFBP3 (deduced from the network topol-

ogy) and the previously reported observation about this

protein [28], a new range of experiments was designed.

We pointed out the IGF2 node in the network since it

involves a prediction of Type II being is a direct conse-

quence of several predictions discussed above.

Indeed, the prediction about IGF2 protein is a three-

step backward deduction. It is due to the combination

of observations on CDKN1C mRNA and PDGFRB

mRNA (Figure 4). The observed variation of CDKN1C

mRNA is ‘+’, and it has two incoming interactions: from

proteins TP73 and IGF2. According to BioQuali, the

observation on CDKN1C mRNA must be explained by

one of these entries. It cannot be explained by regula-

tion from the TP73 protein (prediction addressed in

details above). Therefore, the only possible explanation

of the observed variation of CDKN1C mRNA is its inhi-

bition by IGF2. In this case, the variation of the IGF2

protein must be ‘-’, as it is stated in the prediction list-

ing (see Table 2 and Additional file 5: Results of the

constraint-based analysis). Pushing the reasoning

further, the predicted variation of IGF2 protein must be

explained by one of its incoming interactions, either by

the inhibition from the IGFBP3 protein or by the activa-

tion from IGF2 mRNA (Figure 4A). According to the

prediction about IGFBP3 protein, IGF2 mRNA can be

the only explanation of predicted variation of IGF2 pro-

tein (prediction: IGF2 mRNA = ‘-’).

In other words, according to the reasoning we have

used up to now, both IGF2 and its mRNA should

decrease during the oncogene inhibition, and this beha-

vior is shown to be characteristic from the network

topology and Affymetrix observations. Both these varia-

tions were checked experimentally. RT QPCR showed

that the level of IGF2 mRNA increases during the inhi-

bition of EWS-FLI1 (Figure 5) and Western blotting

showed that IGF2 protein does not respond significantly

to the inhibition of EWS-FLI1 (Figure 6). As for obser-

vation from [28], this was in complete disagreement

with the variations deduced from the topology of the

model.

Re-performing the analysis and pointing out lacks in the

network

A new set of observations was built according to the

new data at hand: the significant variations observed in

the Affymetrix chip were complemented by two addi-

tional data: ‘IGF2_mRNA = +’ and ‘IGFBP3 = +’. The

BioQuali analysis of the network with respect to this set

of variations resulted in an inconsistent diagnosis, indi-

cating that regulations are missing over IGF1 to explain

the new set of observations. This points out the need

for further biological investigations of this pathway to

complete the picture of regulations and pathway cross-

talks in Ewing’s cancer.

This situation illustrates the utility of our approach,

which points out weak parts of the model that should

be submitted for thorough experimentation. The con-

flicts discovered also suggest those parts of the network

model (a priori generic) which should be adapted to the

specificities of the cell line and phenotype. In this case,

our theoretical investigations of the EWS-FLI1 regula-

tory network suggest that the IGF pathway could be an

important factor in the development of Ewing’s cancer.

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This agrees with previously reported Identification of

IGF1 and IGFBP3 as EWS-FLI1 target genes in [28-31].

ConclusionWe have used a network representation to model the

behavior of genes controlled by an oncogene in the con-

text of Ewing’s sarcoma. The network gathers “gold

standard” but generic information on pathways involved

in cell proliferation and apoptosis, which are putatively

under the influence of the oncogene. The network has

been modified to include post-translational interactions

and to contain both mRNA and protein nodes. The ana-

lysis allowed us to pinpoint active interactions specific

to this cancer. We have also identified those parts of the

network which were incomplete and should be sub-

mitted for further investigation.

The analysis was performed using the open-source

software tool BioQuali and consisted of an automatic

constraint-based analysis mimicking biologist’s local rea-

soning on a large scale. The logical constraints used by

the tool were proved to be relevant for our experimental

setting - steady state shifts - in a previous mathematical

work [24].

The results from the tool were post-processed by (1)

differentiating between transcriptional and post-transla-

tional products of gene expression (2) analyzing the pre-

dicted variations to localize potentially active regulations

and suggest network corrections. The underlying idea

behind this process was that relevant information will

be derived from situations where the naive application

of the transcription dogma in eukaryotes could not

apply.

The originality of our regulatory network analysis lies

in the combination of prediction and localization of

inconsistencies and contradictions. Predictions enhance

the set of observable nodes by including products that

are not accessible to experiments (such as active pro-

teins in transcriptomic studies) or for which the accu-

racy of measurement is insufficient. An enhanced

network can render complementary analysis such as

sub-network classification methods and hypothesis gen-

eration more efficient. It also pinpoints active interac-

tions and reveals disease specificities. Network failures

and inconsistencies such as missing interactions or

errors in node interpretation can also be localized in

our approach. Corrections and further experiments are

proposed in this case.

Last but not least, this type of analysis is iterative. As

with any predictive method in systems biology, the

results of the consistency checking depend on both the

network and experimental data at hand: an inconsis-

tency diagnosis locates lacunae in the model with

respect to available data; predictions are direct

consequences of those available data. Therefore, switch-

ing to another dataset may considerably modify the set

of predictions and even change the result of the consis-

tency checking if the network topology fails to explain

the new set of inputs. Concretely, predictions in a first

step can become inconsistencies when confronted with

new data and require network correction in further

steps. This happened in present work: applying the

method to Ewing’s sarcoma network and data we sug-

gest that the full dataset explains a few post-transla-

tional processes. The post-processing of the results of

the analysis led to proposition of the complementary

experiments. These complementary experiments vali-

dated some of the predictions but also revealed lacks in

the generic network. We have confirmed the specific

role of IGF in the development of Ewing’s cancer and

have localized parts of this pathway that should be stu-

died as a priority in the future.

More generally, it is crucial to build networks in a

manner that realistically takes into account various

sources of expression variation (transcription factors

activities, alternative splicing, post-translational modifi-

cations of proteins, etc.). Thus, other elements shall be

considered in a network structure, and extensions are in

progress. These may be microRNAs, mRNAs, proteins,

phosphorylated proteins, etc. depending on the type of

the experimental data available to the modeler. For

instance, our formalism can easily integrate the effect of

genetic amplifications by introducing DNA node type

whose attribute is the gene copy number variation

(work in progress). Alternative splicing is simply a ques-

tion of multiplying the proteins nodes resulting from a

given mRNA. Our approach is a first step forward in

this direction.

Additional material

Additional file 1: Interaction graph adapted from original network.

The non-modified interaction graph (no differentiation between mRNAs

and proteins done) is shown in the file ‘Additional file 1.csv’, which is

structured to be readable by the BioQuali tool.

Additional file 2: Modified interaction graph. ‘Additional file 2.cvs’

contains the network which includes all modifications as described in

the text of the paper. It is also in a BioQuali-readable format of

interaction network representation.

Additional file 3: Results of statistical analysis of microarray data

[21]. ‘Additional file 3.xls’ comprises calculated slopes, variations and

corresponding p-values for microarray data of gene responses on both

inhibition and recovery of EWS-FLI1. All the transcript variants of genes

present in our network model were grouped and the statistical tests

were applied to such ‘gene’ data rather than ‘transcript variant’ data, to

give a comprehensive picture of gene behaviors.

Additional file 4: Set of experimental observations. ‘Additional file 4.

csv’ file presents the observation dataset resulting from the statistical

analysis of microarray data shown in Additional file 3.

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Additional file 5: Results of the constraint-based analysis. ‘Additional

file 5.xls’ contains the results of constraint-based modeling of the

extended EWS-FLI1 regulatory network with the integrated experimental

observations set. The first column shows the discovered inconsistencies

(while analyzing the original interaction network). The following columns

display the consistency of the modified network (mRNA nodes included)

with the dataset and predictions obtained after the analysis.

Acknowledgements

The authors thank Prof. E. Barillot, Dr. A. Zinoviev, Dr. F. Tirode and Dr. K.

Laud-Duval at Institut Curie in Paris as well as Prof. T. Sauter and Dr. M.

Wiesinger at University of Luxembourg for their invaluable help, discussions

and collaboration. The authors thank University of Luxembourg, and

particularly Professor D. Ferring and Dr. M. Baumann (Ass. Prof.) for the

payment of the article processing charges. They also wish to thank Dr. M. Le

Borgne for implementing the BioQuali server side component, and C.

Guziolowski and the GenOuest bioinformatics platform for the technical

support. This work was supported by the ANR-06-BYOS-0004 SITCON project.

Author details1Systems Biology Group, Life Science Research Unit, University of

Luxembourg,162A Avenue de la Faiencerie, Luxembourg, L-1511,

Luxembourg. 2Work done at: IRMAR, Université de Rennes 1, Rennes, France.3UMR 6625, CNRS, Rennes, France. 4Genetics and Biology of Cancers, Institut

Curie, Paris, France. 5Unité 830, INSERM, Paris, France. 6Service

bioinformatique, Institut Curie, Paris, France. 7Unité 900, INSERM, Paris,

France. 8Service bioinformatique, Mines ParisTech, Fontainebleau, France.9DIMNP, Université de Montpellier 2, Montpellier, France. 10UMR 5235, CNRS,

Montpellier, France. 11Symbiose project team, INRIA, Rennes, France. 12UMR

6074, CNRS, Rennes, France. 13IRISA, Université de Rennes 1, Rennes, France.

Authors’ contributions

TB conceived the study and performed the analysis. DS and OD produced

experimental data for predictions validation. TB, BD and OR performed

statistical analysis of microarray data. GS built the regulatory network, further

adjusted by TB for this study. TB, OR and AS wrote the manuscript. AS

guided the project. All authors read, corrected and approved the final

manuscript.

Received: 17 February 2010 Accepted: 2 November 2010

Published: 2 November 2010

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doi:10.1186/1752-0509-4-146Cite this article as: Baumuratova et al.: Localizing potentially active post-transcriptional regulations in the Ewing’s sarcoma gene regulatorynetwork. BMC Systems Biology 2010 4:146.

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